Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

Abstract

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models' robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability.

MDPI Mach. Learn. Knowl. Extr. 2021, 3(2), 333-356; https://www.mdpi.com/2624-800X/1/2/14

References in corpus (7)

• Scikit-learn: Machine Learning in Python
• TensorFlow: A system for large-scale machine learning
• Explaining and Harnessing Adversarial Examples
• Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
• Support Vector Machines under Adversarial Label Contamination
• Maximal Jacobian-based Saliency Map Attack
• A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms

Cited by in corpus (6)

• Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
• Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review
• Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification
• SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection
• Ransomware: Analysing the Impact on Windows Active Directory Domain Services
• Hack The Box: Fooling Deep Learning Abstraction-Based Monitors